The brain-protective mechanism of fecal microbiota transplantation from young donor mice in the natural aging process via exosome, gut microbiota, and metabolomics analyses

代谢组学 移植 肠道菌群 阿克曼西亚 氧化应激 生物 微生物学 免疫学 细菌 医学 微泡 乳酸菌 生物信息学 小RNA 遗传学 生物化学 内科学 基因
作者
Longfei Lin,Ruying Tang,Yuling Liu,Zhiyong Li,Hui Li,Hongjun Yang
出处
期刊:Pharmacological Research [Elsevier BV]
卷期号:207: 107323-107323 被引量:9
标识
DOI:10.1016/j.phrs.2024.107323
摘要

The natural aging process is accompanied by changes in exosomes, gut microbiota, and metabolites. This study aimed to reveal the anti-aging effect and mechanisms of fecal microbiota transplantation (FMT) from young donors on the natural aging process in mice by analyzing exosomes, gut microbiota, and metabolomics. Aging-relevant telomeric length, oxidative stress indexes in brain tissue, and serum cytokine levels were measured. Flow analysis of T-regulatory (Treg), CD4+, and CD8+ cells was performed, and the expression levels of aging-related proteins were quantified. High-throughput sequencing technology was used to identify differentially expressed serum exosomal miRNAs. Fecal microbiota was tested by 16 S rDNA sequencing. Changes in fecal metabolites were analyzed by UPLC-Q-TOF/MS. The results indicated that the expression of mmu-miR-7010-5p, mmu-miR-376b-5p, mmu-miR-135a-5p, and mmu-miR-3100-5p by serum exosomes was down-regulated and the abundance of opportunistic bacteria (Turicibacter, Allobaculum, Morganella.) was decreased, whereas the levels of protective bacteria (Akkermansia, Muribaculaceae, Helicobacter.) were increased after FMT. Metabolic analysis identified 25 potential biomarkers. Correlation analysis between the gut microbiota and metabolites suggested that the relative abundance of protective bacteria was positively correlated with the levels of spermidine and S-adenosylmethionine. The study indicated that FMT corrected brain injury due to aging via lipid metabolism, the metabolism of cofactors and vitamins, and amino acid metabolism.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
stagger发布了新的文献求助10
1秒前
2秒前
177发布了新的文献求助10
4秒前
6秒前
默默访冬完成签到 ,获得积分10
6秒前
依依牙我在做什么完成签到,获得积分10
6秒前
超帅靖雁发布了新的文献求助10
7秒前
8秒前
Qin完成签到,获得积分10
9秒前
小葵发布了新的文献求助10
11秒前
14秒前
沚沐发布了新的文献求助10
15秒前
D调的华丽完成签到,获得积分10
16秒前
Qin应助柏果采纳,获得10
16秒前
爱撒娇的大开完成签到 ,获得积分10
18秒前
风再起时发布了新的文献求助10
19秒前
19秒前
20秒前
yyy完成签到,获得积分10
21秒前
22秒前
22秒前
漪涙应助美好的弘文采纳,获得20
24秒前
陌路发布了新的文献求助10
24秒前
24秒前
彭于晏应助咖可乐采纳,获得10
25秒前
顾矜应助177采纳,获得10
25秒前
风再起时完成签到,获得积分10
25秒前
圈地自萌X发布了新的文献求助10
26秒前
Orange应助聪明的中心采纳,获得10
26秒前
27秒前
29秒前
30秒前
31秒前
ma3501134992应助科研渣渣采纳,获得10
31秒前
32秒前
老艺人发布了新的文献求助10
33秒前
Jiangpeng发布了新的文献求助10
34秒前
34秒前
36秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443660
求助须知:如何正确求助?哪些是违规求助? 8257454
关于积分的说明 17587015
捐赠科研通 5502315
什么是DOI,文献DOI怎么找? 2900945
邀请新用户注册赠送积分活动 1877987
关于科研通互助平台的介绍 1717534